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High-Throughput Screening Test for Adhesion in Soft Materials Using Centrifugation.

ABSTRACT: High-throughput screening of mechanical properties can transform materials science research by both aiding in materials discovery and developing predictive models. However, only a few such assays have been reported, requiring custom or expensive equipment, while the mounting demand for enormous data sets of materials properties for predictive models is unfulfilled by the current characterization throughput. We address this problem by developing a high-throughput colorimetric adhesion screening method using a common laboratory centrifuge, multiwell plates, and microparticles. The technique uses centrifugation to apply a homogeneous mechanical detachment force across individual formulations in a multiwell plate. We also develop a high-throughput sample deposition method to prepare films with uniform thickness in each well, minimizing well-to-well variability. After establishing excellent agreement with the well-known probe tack adhesion test, we demonstrate the consistency of our method by performing the test on a multiwell plate with two different formulations in an easily discernible pattern. The throughput is limited only by the number of wells in the plates, easily reaching 10³ samples/run. With its simplicity, low cost, and large dynamic range, this high-throughput method has the potential to change the landscape of adhesive material characterization.

SUBMITTER: Chen Y

PROVIDER: S-EPMC8323114 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:The materials genome initiative has led to the creation of a large (over a million) database of different classes of nanoporous materials. As the number of hypothetical materials that can, in principle, be experimentally synthesized is infinite, a bottleneck in the use of these databases for the discovery of novel materials is the lack of efficient computational tools to analyze them. Current approaches use brute-force molecular simulations to generate thermodynamic data needed to predict the performance of these materials in different applications, but this approach is limited to the analysis of tens of thousands of structures due to computational intractability. As such, it is conceivable and even likely that the best nanoporous materials for any given application have yet to be discovered both experimentally and theoretically. In this article, we seek a computational approach to tackle this issue by transitioning away from brute-force characterization to high-throughput screening methods based on big-data analysis, using the zeolite database as an example. For identifying and comparing zeolites, we used a topological data analysis-based descriptor (TD) recognizing pore shapes. For methane storage and carbon capture applications, our analyses seeking pairs of highly similar zeolites discovered good correlations between performance properties of a seed zeolite and the corresponding pair, which demonstrates the capability of TD to predict performance properties. It was also shown that when some top zeolites are known, TD can be used to detect other high-performing materials as their neighbors with high probability. Finally, we performed high-throughput screening of zeolites based on TD. For methane storage (or carbon capture) applications, the promising sets from our screenings contained high-percentages of top-performing zeolites: 45% (or 23%) of the top 1% zeolites in the entire set. This result shows that our screening approach using TD is highly efficient in finding high-performing materials. We expect that this approach could easily be extended to other applications by simply adjusting one parameter, the size of the target gas molecule.

Dataset Information

High-Throughput Screening Test for Adhesion in Soft Materials Using Centrifugation.

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